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     "text": [
      "2024-05-12 10:19:24.170724: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-05-12 10:19:24.170815: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-05-12 10:19:24.298363: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start on cuda:0 device.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torchvision import models, transforms\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(\"start on {} device.\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d9c247b",
   "metadata": {
    "execution": {
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   "source": [
    "# 保存训练模型名称\n",
    "save_model_name = \"vgg16_base_cifar10\"\n",
    "# 训练的轮数\n",
    "epoch = 50\n",
    "# 一批数据大小\n",
    "batch_size = 128\n",
    "# 丢弃率\n",
    "dropout = 0.5\n",
    "# 数据集路径\n",
    "# /kaggle/input/dogs-vs-cats\n",
    "dataset_path = \"./dataset\"\n",
    "# tensorboard路径\n",
    "writer = SummaryWriter(\"./logs\")\n",
    "# 学习率\n",
    "# learning_rate = 0.01\n",
    "# 1e-2=1 x (10)^(-2) = 1 /100 = 0.01\n",
    "learning_rate = 1e-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d98ebc83",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./dataset/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 170498071/170498071 [00:03<00:00, 49647392.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./dataset/cifar-10-python.tar.gz to ./dataset\n",
      "Files already downloaded and verified\n",
      "训练数据集长度: 50000\n",
      "测试数据集长度: 10000\n",
      "torch.Size([128, 3, 32, 32])\n",
      "torch.Size([128, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.ToTensor()\n",
    "train_data = torchvision.datasets.CIFAR10(dataset_path, train=True, download=True,\n",
    "                                          transform=transform)\n",
    "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=False)\n",
    "\n",
    "test_data = torchvision.datasets.CIFAR10(dataset_path, train=False, download=True,\n",
    "                                         transform=transform)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=False)\n",
    "\n",
    "train_data_size = len(train_data)\n",
    "test_data_size = len(test_data)\n",
    "print(\"训练数据集长度: {}\".format(train_data_size))\n",
    "print(\"测试数据集长度: {}\".format(test_data_size))\n",
    "\n",
    "for data in train_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break\n",
    "\n",
    "for data in test_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break"
   ]
  },
  {
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   "source": [
    "class VGG16_BASE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VGG16_BASE, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # vgg16原本时 nn.AdaptiveAvgPool2d((7, 7))，由于输入图片大小导致\n",
    "            # nn.AvgPool2d(kernel_size=1, stride=1)\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            # 原始模型vgg16输入image大小是224 x 224\n",
    "            # CIFAR10输入image大小是32 x 32\n",
    "            # 大小是小了 7 x 7倍\n",
    "            nn.Linear(512, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.features(x)\n",
    "        # output = output.view(output.size(0), -1)\n",
    "        output = torch.flatten(output, 1)\n",
    "        output = self.classifier(output)\n",
    "        return output"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16_BASE(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (12): ReLU(inplace=True)\n",
      "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (16): ReLU(inplace=True)\n",
      "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (19): ReLU(inplace=True)\n",
      "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (26): ReLU(inplace=True)\n",
      "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (32): ReLU(inplace=True)\n",
      "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (36): ReLU(inplace=True)\n",
      "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (39): ReLU(inplace=True)\n",
      "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (42): ReLU(inplace=True)\n",
      "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=512, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 网络模型相关初始化\n",
    "# 创建网络模型\n",
    "mymod = VGG16_BASE()\n",
    "print(mymod)\n",
    "\n",
    "mymod = mymod.to(device)\n",
    "# 损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "# loss_fn = loss_fn.to(device)\n",
    "\n",
    "# 优化器\n",
    "optimizer = torch.optim.SGD(mymod.parameters(), lr=learning_rate, weight_decay=1e-6)\n",
    "# 学习率衰减，每10轮减少90%\n",
    "scheduler = StepLR(optimizer, step_size=10, gamma=0.1) "
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start train.\n",
      "第0轮训练用时：15.14085078239441 s\n",
      "第0轮训练loss：1.4852505982780457\n",
      "第0轮训练精度：0.45208\n",
      "------\n",
      "第0轮测试用时：2.303466320037842 s\n",
      "第0轮测试loss：1.2731296634674072\n",
      "第0轮测试精度：0.5479\n",
      "------\n",
      "第1轮训练用时：14.161638736724854 s\n",
      "第1轮训练loss：0.859331456413269\n",
      "第1轮训练精度：0.70346\n",
      "------\n",
      "第1轮测试用时：2.262573719024658 s\n",
      "第1轮测试loss：1.0227216049194336\n",
      "第1轮测试精度：0.6539\n",
      "------\n",
      "第2轮训练用时：14.16227388381958 s\n",
      "第2轮训练loss：0.6263658154392242\n",
      "第2轮训练精度：0.78828\n",
      "------\n",
      "第2轮测试用时：2.267709970474243 s\n",
      "第2轮测试loss：1.0313318895339967\n",
      "第2轮测试精度：0.6536\n",
      "------\n",
      "第3轮训练用时：14.154839515686035 s\n",
      "第3轮训练loss：0.4812595875644684\n",
      "第3轮训练精度：0.83836\n",
      "------\n",
      "第3轮测试用时：2.312406539916992 s\n",
      "第3轮测试loss：1.1550979481697083\n",
      "第3轮测试精度：0.6568\n",
      "------\n",
      "第4轮训练用时：14.170994758605957 s\n",
      "第4轮训练loss：0.3835806956386566\n",
      "第4轮训练精度：0.87104\n",
      "------\n",
      "第4轮测试用时：2.243238925933838 s\n",
      "第4轮测试loss：2.0018249032974245\n",
      "第4轮测试精度：0.5682\n",
      "------\n",
      "第5轮训练用时：14.180075407028198 s\n",
      "第5轮训练loss：0.3059413129377365\n",
      "第5轮训练精度：0.8964\n",
      "------\n",
      "第5轮测试用时：2.3067269325256348 s\n",
      "第5轮测试loss：0.5980473843574524\n",
      "第5轮测试精度：0.8109\n",
      "------\n",
      "第6轮训练用时：14.196285247802734 s\n",
      "第6轮训练loss：0.23901207320690154\n",
      "第6轮训练精度：0.9201\n",
      "------\n",
      "第6轮测试用时：2.2480571269989014 s\n",
      "第6轮测试loss：0.7313045894622803\n",
      "第6轮测试精度：0.7896\n",
      "------\n",
      "第7轮训练用时：14.151123523712158 s\n",
      "第7轮训练loss：0.1899646280193329\n",
      "第7轮训练精度：0.93548\n",
      "------\n",
      "第7轮测试用时：2.2644009590148926 s\n",
      "第7轮测试loss：0.7355887553215027\n",
      "第7轮测试精度：0.7952\n",
      "------\n",
      "第8轮训练用时：14.149187088012695 s\n",
      "第8轮训练loss：0.14821007046222687\n",
      "第8轮训练精度：0.94976\n",
      "------\n",
      "第8轮测试用时：2.246953248977661 s\n",
      "第8轮测试loss：0.6850190131664277\n",
      "第8轮测试精度：0.8055\n",
      "------\n",
      "第9轮训练用时：14.22871994972229 s\n",
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      "第9轮训练精度：0.96052\n",
      "------\n",
      "第9轮测试用时：2.293584108352661 s\n",
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      "------\n",
      "第10轮训练用时：14.159596681594849 s\n",
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      "第10轮训练精度：0.98854\n",
      "------\n",
      "第10轮测试用时：2.260301113128662 s\n",
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      "------\n",
      "第11轮训练用时：14.19172978401184 s\n",
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      "第11轮训练精度：0.99744\n",
      "------\n",
      "第11轮测试用时：2.2503745555877686 s\n",
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      "第11轮测试精度：0.8694\n",
      "------\n",
      "第12轮训练用时：14.160058975219727 s\n",
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      "------\n",
      "第12轮测试用时：2.2483623027801514 s\n",
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      "------\n",
      "第13轮训练用时：14.196353673934937 s\n",
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      "------\n",
      "第13轮测试用时：2.262538433074951 s\n",
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      "------\n",
      "第14轮训练用时：14.1582510471344 s\n",
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      "------\n",
      "第14轮测试用时：2.246811866760254 s\n",
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      "------\n",
      "第15轮训练用时：14.211949586868286 s\n",
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      "------\n",
      "第15轮测试用时：2.305875301361084 s\n",
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      "------\n",
      "第16轮训练用时：14.178394317626953 s\n",
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      "------\n",
      "第16轮测试用时：2.2393624782562256 s\n",
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      "------\n",
      "第17轮训练用时：14.203913688659668 s\n",
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      "------\n",
      "第17轮测试用时：2.2517082691192627 s\n",
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      "------\n",
      "第18轮训练用时：14.174252033233643 s\n",
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      "------\n",
      "第18轮测试用时：2.256761312484741 s\n",
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      "------\n",
      "第19轮训练用时：14.208332300186157 s\n",
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      "------\n",
      "第19轮测试用时：2.2451910972595215 s\n",
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      "------\n",
      "第20轮训练用时：14.156002044677734 s\n",
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      "------\n",
      "第20轮测试用时：2.2442617416381836 s\n",
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      "------\n",
      "第21轮训练用时：14.178653717041016 s\n",
      "第21轮训练loss：0.0010389694880507886\n",
      "第21轮训练精度：0.9999\n",
      "------\n",
      "第21轮测试用时：2.2696073055267334 s\n",
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      "------\n",
      "第22轮训练用时：14.165494918823242 s\n",
      "第22轮训练loss：0.0010401980944722891\n",
      "第22轮训练精度：0.9999\n",
      "------\n",
      "第22轮测试用时：2.2519826889038086 s\n",
      "第22轮测试loss：0.6678259829044342\n",
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      "------\n",
      "第23轮训练用时：14.194266319274902 s\n",
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      "第23轮训练精度：0.99992\n",
      "------\n",
      "第23轮测试用时：2.2372682094573975 s\n",
      "第23轮测试loss：0.6680913464546203\n",
      "第23轮测试精度：0.8704\n",
      "------\n",
      "第24轮训练用时：14.16843318939209 s\n",
      "第24轮训练loss：0.0010856196230556816\n",
      "第24轮训练精度：0.99986\n",
      "------\n",
      "第24轮测试用时：2.348562240600586 s\n",
      "第24轮测试loss：0.6672159713745117\n",
      "第24轮测试精度：0.8702\n",
      "------\n",
      "第25轮训练用时：14.172224521636963 s\n",
      "第25轮训练loss：0.001016528708776459\n",
      "第25轮训练精度：0.99992\n",
      "------\n",
      "第25轮测试用时：2.243774652481079 s\n",
      "第25轮测试loss：0.6708303846359253\n",
      "第25轮测试精度：0.8697\n",
      "------\n",
      "第26轮训练用时：14.17913007736206 s\n",
      "第26轮训练loss：0.0010066002114117146\n",
      "第26轮训练精度：0.99994\n",
      "------\n",
      "第26轮测试用时：2.260434150695801 s\n",
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      "第26轮测试精度：0.8703\n",
      "------\n",
      "第27轮训练用时：14.166242122650146 s\n",
      "第27轮训练loss：0.0008613319795764983\n",
      "第27轮训练精度：0.99994\n",
      "------\n",
      "第27轮测试用时：2.2735118865966797 s\n",
      "第27轮测试loss：0.6656240493297577\n",
      "第27轮测试精度：0.87\n",
      "------\n",
      "第28轮训练用时：14.211139917373657 s\n",
      "第28轮训练loss：0.0007921952868066728\n",
      "第28轮训练精度：1.0\n",
      "------\n",
      "第28轮测试用时：2.2585318088531494 s\n",
      "第28轮测试loss：0.6712493875980378\n",
      "第28轮测试精度：0.8703\n",
      "------\n",
      "第29轮训练用时：14.197786331176758 s\n",
      "第29轮训练loss：0.0009388243881799281\n",
      "第29轮训练精度：0.99994\n",
      "------\n",
      "第29轮测试用时：2.239813804626465 s\n",
      "第29轮测试loss：0.6747901483058929\n",
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      "------\n",
      "第30轮训练用时：14.18799114227295 s\n",
      "第30轮训练loss：0.001025670108254999\n",
      "第30轮训练精度：0.99986\n",
      "------\n",
      "第30轮测试用时：2.235166549682617 s\n",
      "第30轮测试loss：0.6751230783939361\n",
      "第30轮测试精度：0.8707\n",
      "------\n",
      "第31轮训练用时：14.168960571289062 s\n",
      "第31轮训练loss：0.0009734614219516516\n",
      "第31轮训练精度：0.99988\n",
      "------\n",
      "第31轮测试用时：2.262807607650757 s\n",
      "第31轮测试loss：0.6709305583000184\n",
      "第31轮测试精度：0.8706\n",
      "------\n",
      "第32轮训练用时：14.189614534378052 s\n",
      "第32轮训练loss：0.0008607760838419199\n",
      "第32轮训练精度：0.99998\n",
      "------\n",
      "第32轮测试用时：2.244345188140869 s\n",
      "第32轮测试loss：0.6744969731807708\n",
      "第32轮测试精度：0.8712\n",
      "------\n",
      "第33轮训练用时：14.157120704650879 s\n",
      "第33轮训练loss：0.0008425485156849027\n",
      "第33轮训练精度：0.99996\n",
      "------\n",
      "第33轮测试用时：2.2614083290100098 s\n",
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      "------\n",
      "第34轮训练用时：14.224722623825073 s\n",
      "第34轮训练loss：0.0008529857038334012\n",
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      "------\n",
      "第34轮测试用时：2.232858180999756 s\n",
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      "------\n",
      "第35轮训练用时：14.152825355529785 s\n",
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      "------\n",
      "第35轮测试用时：2.255725860595703 s\n",
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      "------\n",
      "第36轮训练用时：14.178606271743774 s\n",
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      "第36轮训练精度：0.99986\n",
      "------\n",
      "第36轮测试用时：2.248636245727539 s\n",
      "第36轮测试loss：0.6691747372150421\n",
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      "------\n",
      "第37轮训练用时：14.186333656311035 s\n",
      "第37轮训练loss：0.0009461605709418654\n",
      "第37轮训练精度：0.99992\n",
      "------\n",
      "第37轮测试用时：2.234891414642334 s\n",
      "第37轮测试loss：0.6703571833133698\n",
      "第37轮测试精度：0.8706\n",
      "------\n",
      "第38轮训练用时：14.200249195098877 s\n",
      "第38轮训练loss：0.0009098968007974327\n",
      "第38轮训练精度：0.99998\n",
      "------\n",
      "第38轮测试用时：2.276624917984009 s\n",
      "第38轮测试loss：0.671316109418869\n",
      "第38轮测试精度：0.8703\n",
      "------\n",
      "第39轮训练用时：14.172969341278076 s\n",
      "第39轮训练loss：0.0008039694004319608\n",
      "第39轮训练精度：0.99998\n",
      "------\n",
      "第39轮测试用时：2.303048849105835 s\n",
      "第39轮测试loss：0.6736525240421295\n",
      "第39轮测试精度：0.871\n",
      "------\n",
      "第40轮训练用时：14.220389604568481 s\n",
      "第40轮训练loss：0.0009298074096441269\n",
      "第40轮训练精度：0.99994\n",
      "------\n",
      "第40轮测试用时：2.248377561569214 s\n",
      "第40轮测试loss：0.6707411728382111\n",
      "第40轮测试精度：0.8697\n",
      "------\n",
      "第41轮训练用时：14.17469334602356 s\n",
      "第41轮训练loss：0.0009243274579755962\n",
      "第41轮训练精度：0.99992\n",
      "------\n",
      "第41轮测试用时：2.2903194427490234 s\n",
      "第41轮测试loss：0.6785758946418762\n",
      "第41轮测试精度：0.8697\n",
      "------\n",
      "第42轮训练用时：14.188687324523926 s\n",
      "第42轮训练loss：0.000909946711473167\n",
      "第42轮训练精度：0.99996\n",
      "------\n",
      "第42轮测试用时：2.2516283988952637 s\n",
      "第42轮测试loss：0.6686698944091797\n",
      "第42轮测试精度：0.8705\n",
      "------\n",
      "第43轮训练用时：14.200217008590698 s\n",
      "第43轮训练loss：0.00098314001086168\n",
      "第43轮训练精度：0.99988\n",
      "------\n",
      "第43轮测试用时：2.3127846717834473 s\n",
      "第43轮测试loss：0.6766458559036255\n",
      "第43轮测试精度：0.8712\n",
      "------\n",
      "第44轮训练用时：14.18194842338562 s\n",
      "第44轮训练loss：0.0008992813273891807\n",
      "第44轮训练精度：0.99992\n",
      "------\n",
      "第44轮测试用时：2.276684284210205 s\n",
      "第44轮测试loss：0.6789490044116974\n",
      "第44轮测试精度：0.8698\n",
      "------\n",
      "第45轮训练用时：14.202955722808838 s\n",
      "第45轮训练loss：0.0008209523364342749\n",
      "第45轮训练精度：0.99994\n",
      "------\n",
      "第45轮测试用时：2.258770227432251 s\n",
      "第45轮测试loss：0.6733082737445831\n",
      "第45轮测试精度：0.8712\n",
      "------\n",
      "第46轮训练用时：14.18501091003418 s\n",
      "第46轮训练loss：0.0009026711282692849\n",
      "第46轮训练精度：0.99994\n",
      "------\n",
      "第46轮测试用时：2.2776663303375244 s\n",
      "第46轮测试loss：0.6696615381717682\n",
      "第46轮测试精度：0.8716\n",
      "------\n",
      "第47轮训练用时：14.197028160095215 s\n",
      "第47轮训练loss：0.0008873627320304513\n",
      "第47轮训练精度：0.9999\n",
      "------\n",
      "第47轮测试用时：2.2456321716308594 s\n",
      "第47轮测试loss：0.6731224643707275\n",
      "第47轮测试精度：0.8705\n",
      "------\n",
      "第48轮训练用时：14.17048168182373 s\n",
      "第48轮训练loss：0.0009071909658983349\n",
      "第48轮训练精度：0.99994\n",
      "------\n",
      "第48轮测试用时：2.252202272415161 s\n",
      "第48轮测试loss：0.6715901633262634\n",
      "第48轮测试精度：0.8716\n",
      "------\n",
      "第49轮训练用时：14.233739137649536 s\n",
      "第49轮训练loss：0.0010702926746942103\n",
      "第49轮训练精度：0.99988\n",
      "------\n",
      "第49轮测试用时：2.248833417892456 s\n",
      "第49轮测试loss：0.6771819163322449\n",
      "第49轮测试精度：0.8706\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "# 保存训练数据绘图\n",
    "train_loss_list = []\n",
    "train_accuracy_list = []\n",
    "\n",
    "test_loss_list = []\n",
    "test_accuracy_list = []\n",
    "\n",
    "epoch_step = []\n",
    "print(\"start train.\")\n",
    "for i in range(epoch):\n",
    "    epoch_step.append(i)\n",
    "    \n",
    "    mymod.train()\n",
    "    start_time = time.time()\n",
    "    total_train_loss_pre_epoch = 0\n",
    "    total_train_accuracy_pre_epoch = 0\n",
    "    for data in train_dataloader:\n",
    "        imgs, targets = data\n",
    "        # print(imgs.shape)\n",
    "        imgs = imgs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        outputs = mymod(imgs)\n",
    "        accuracy = (outputs.argmax(1) == targets).sum()\n",
    "        total_train_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "        loss = loss_fn(outputs, targets)\n",
    "        total_train_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    end_train_time = time.time()\n",
    "    print(\"第{}轮训练用时：{} s\".format(i, end_train_time - start_time))\n",
    "    print(\"第{}轮训练loss：{}\".format(i, total_train_loss_pre_epoch / train_data_size))\n",
    "    print(\"第{}轮训练精度：{}\".format(i, total_train_accuracy_pre_epoch / train_data_size))\n",
    "    print(\"------\")\n",
    "    train_loss_list.append(total_train_loss_pre_epoch / train_data_size)\n",
    "    train_accuracy_list.append(total_train_accuracy_pre_epoch / train_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"train_loss\", total_train_loss_pre_epoch / train_data_size, i)\n",
    "    writer.add_scalar(\"train_acc\", total_train_accuracy_pre_epoch / train_data_size, i)\n",
    "    # 更新学习率\n",
    "    scheduler.step()\n",
    "    mymod.eval()\n",
    "    total_test_loss_pre_epoch = 0\n",
    "    total_test_accuracy_pre_epoch = 0\n",
    "    # 去掉梯度\n",
    "    with torch.no_grad():\n",
    "        for data in test_dataloader:\n",
    "            imgs, targets = data\n",
    "            imgs = imgs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            outputs = mymod(imgs)\n",
    "\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            total_test_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "            # 统计在测试集上正确的个数，然后累加起来\n",
    "            accuracy = (outputs.argmax(1) == targets).sum()\n",
    "            total_test_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "\n",
    "    end_test_time = time.time()\n",
    "    print(\"第{}轮测试用时：{} s\".format(i, end_test_time - end_train_time))\n",
    "    print(\"第{}轮测试loss：{}\".format(i, total_test_loss_pre_epoch / test_data_size))\n",
    "    print(\"第{}轮测试精度：{}\".format(i, total_test_accuracy_pre_epoch / test_data_size))\n",
    "    print(\"------\")\n",
    "    test_loss_list.append(total_test_loss_pre_epoch / test_data_size)\n",
    "    test_accuracy_list.append(total_test_accuracy_pre_epoch / test_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"test_loss\", total_test_loss_pre_epoch / test_data_size, i)\n",
    "    writer.add_scalar(\"test_acc\", total_test_accuracy_pre_epoch / test_data_size, i)\n",
    "\n",
    "\n",
    "torch.save(mymod, \"./{}.pth\".format(save_model_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18281d59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T10:33:24.049607Z",
     "iopub.status.busy": "2024-05-12T10:33:24.049266Z",
     "iopub.status.idle": "2024-05-12T10:33:25.670428Z",
     "shell.execute_reply": "2024-05-12T10:33:25.669381Z"
    },
    "papermill": {
     "duration": 1.63829,
     "end_time": "2024-05-12T10:33:25.672400",
     "exception": false,
     "start_time": "2024-05-12T10:33:24.034110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plt.plot(x,y)\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list)\n",
    "plt.title(\"train_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_loss_list)\n",
    "plt.title(\"test_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list)\n",
    "plt.title(\"train_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_accuracy_list)\n",
    "plt.title(\"test_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_loss_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_accuracy_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
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   "dataSources": [
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     "sourceId": 3362,
     "sourceType": "competition"
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   ],
   "dockerImageVersionId": 30699,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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   "duration": 854.445638,
   "end_time": "2024-05-12T10:33:28.167176",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-05-12T10:19:13.721538",
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